Real-time identification and provision of preferred flight parameters

ABSTRACT

Real-time identification and provision of preferred flight parameters is provided by obtaining flight data of aircraft flights and classifying the flight data according to categories, acquiring current flight parameters from devices of an aircraft during an in-process flight, comparing the current flight parameters to the classified flight data and identifying, in real-time during the in-process flight, and based on thresholds in correlations between the current flight parameters and the classified flight data, preferred action(s) to take and preferred flight parameter value(s) for the in-process flight given current conditions of the aircraft and surrounding environment as reflected by the current flight parameters, and providing the preferred flight parameter values to computer system(s) of the aircraft.

BACKGROUND

Current airplanes and other aircraft include systems that can generateand gather very large amounts of data, in some cases 40-80 terabytes ofdata per hour. Multiplying that data rate for each aircraft of a sizableaircraft fleet, for instance that of a commercial airline, provides anamount of data that is very difficult, and likely impossible, to reviewand process in any reasonable amount of time. However, airlinescurrently allow that data and potentially valuable information to begleaned therefrom go to waste because they are not capable of handlingthat volume of information.

As technology evolves, airplanes include more complex computers andsystems to improve aspects of the flight, such as security andefficiency as examples. However, as noted, the knowledge generated aftereach flight is effectively lost when the pilot leaves the plane. It maybe the case that for a same airplane flying in the same conditions alonga same route different flight results may be realized due to theparticular pilots/crew handling the flight. In other words, pilotexperience and knowledge is relevant in flight execution.

SUMMARY

Shortcomings of the prior art are overcome and additional advantages areprovided through the provision of a computer-implemented method. Themethod obtains flight data of aircraft flights and classifies the flightdata according to categories. The method acquires current flightparameters from devices of an aircraft during an in-process flight. Themethod compares the acquired current flight parameters to the classifiedflight data and identifies, in real-time during the in-process flight,and based on thresholds in correlations between the acquired flightparameters and the classified flight data, preferred action(s) to takeand preferred flight parameter value(s) for the in-process flight givencurrent conditions of the aircraft and surrounding environment asreflected by the acquired current flight parameters. The method alsoprovides the preferred flight parameter values to computer system(s) ofthe aircraft.

Further, a computer system is provided that includes a memory and aprocessor in communication with the memory, where the computer system isconfigured to perform a method. The method obtains flight data ofaircraft flights and classifies the flight data according to categories.The method acquires current flight parameters from devices of anaircraft during an in-process flight. The method compares the acquiredcurrent flight parameters to the classified flight data and identifies,in real-time during the in-process flight, and based on thresholds incorrelations between the acquired flight parameters and the classifiedflight data, preferred action(s) to take and preferred flight parametervalue(s) for the in-process flight given current conditions of theaircraft and surrounding environment as reflected by the acquiredcurrent flight parameters. The method also provides the preferred flightparameter values to computer system(s) of the aircraft.

Yet further, a computer program product including a computer readablestorage medium readable by at least one processor and storinginstructions for execution by the at least one processor is provided forperforming a method. The method obtains flight data of aircraft flightsand classifies the flight data according to categories. The methodacquires current flight parameters from devices of an aircraft during anin-process flight. The method compares the acquired current flightparameters to the classified flight data and identifies, in real-timeduring the in-process flight, and based on thresholds in correlationsbetween the acquired flight parameters and the classified flight data,preferred action(s) to take and preferred flight parameter value(s) forthe in-process flight given current conditions of the aircraft andsurrounding environment as reflected by the acquired current flightparameters. The method also provides the preferred flight parametervalues to computer system(s) of the aircraft.

Additional features and advantages are realized through the conceptsdescribed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects described herein are particularly pointed out and distinctlyclaimed as examples in the claims at the conclusion of thespecification. The foregoing and other objects, features, and advantagesof the invention are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts an example environment to incorporate and/or use aspectsdescribed herein;

FIG. 2 depicts an example conceptual architectural diagram showingcomponents for capture and distribution of experience and knowledgebased on prior flights, in accordance with aspects described herein;

FIG. 3 depicts an example conceptual overview of information sharing inthe capture and distribution of experience and knowledge based on priorflights, in accordance with aspects described herein;

FIG. 4 depicts an example process for real-time identification andprovision of preferred flight parameters, in accordance with aspectsdescribed herein;

FIG. 5 depicts one example of a computer system and associated devicesto incorporate and/or use aspects described herein;

FIG. 6 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Described herein are facilities that capture best practices used byexperienced pilots in given conditions and share that information inreal time to other pilots to enhance security, fuel efficiency, andother aspects across flights of other aircraft. Also provided are waysto leverage the data generated during flights in order to determine bestactions/reactions in emergency situations experienced during in-processflights. Aspects are facilitated using cognitive technology thatintegrates analytics capabilities to leverage the gathered data intouseful information while learning over time. Though examples describedherein may be presented in the context of airplanes (one type ofaircraft) it is understood that aspects described herein have a broaderapplication, for instance to any type of aircraft.

Industrial internet and computer capabilities enable airplanes and otheraircraft to perform at least some onboard analysis and/or sharing ofgathered flight data to cloud-based or other terrestrial computerfacilities. This is in contrast to merely gathering the data and waitinguntil the aircraft lands on the ground to harvest and review it.Real-time monitoring, robust compute power to support all of theon-board sensors, and other capabilities are available.

Current approaches for handling flight data have several disadvantages.They are susceptible to loss or corruption of data, they typicallyrequire dedicated experienced resources to process and analyze the data,the processing and analysis takes a significant amount of time andresources and therefore the information gleaned is obtained long afterthe source data was gathered, they apply limited processing capabilitiesfor big data applications, there is a lack of automation between maincomponents of a system such as the inputs (e.g. aircraft sensors), humananalysis of the data, and the outputs (knowledge gleaned), and there isa lack of, or inefficient distribution of, any useful knowledge gleaned.

Presented herein is the use of a cognitive engine that processes flightdata in real-time and creates a knowledge database with relevant, usefulflight practice information, rather than merely a set of big data thatby itself fails to inform of any viable real-time applicability. Auseful analogy is to the familiar DIKW pyramid and knowledge hierarchy,representing the relationship between ‘data’ at the bottom of thepyramid, ‘information’ above that, ‘knowledge’, and ‘wisdom’ at the topof the pyramid. In the present context, raw data may be analyzed toacquire the information, which is analyzed to create knowledge and bestpractices for aircraft flight parameters in given situations. Areal-time aspect may be significant because of its applicability toin-process flights, i.e. to provide recommendations during a flight toproactively address an undesired situation, rather than a post-hocanalysis of the undesired situation only after it has been experienced.

Aspects described herein can provide real-time suggestions to theaircraft crew (pilot(s) and/or other personnel) leveraging a database ofbest practices created based on the experience of pilots, sensorinformation, previous actions in similar situations, and other items.Real-time suggestions may be with regard to flight parameters (speed,direction, path, and any other parameters including pilot actions,maneuvers, and so on). For instance, parameters that are found to resultin fuel saving for the aircraft given current flight parameters may beprovided as suggested preferred flight parameters.

FIG. 1 depicts an example environment to incorporate and/or use aspectsdescribed herein, such as an environment in which a cognitive system isleveraged in the capture and distribution of experience and knowledge.Airplane 102 is in communication with cognitive system backend server(s)106 via network(s) 108. In practice, airplane 102 includes componentssuch as sensors, computer systems, or the like that communicate withcomponents of network(s) 108.

Cognitive system backend server(s) 106 are computer systems andassociated modules that provide a backend to a cognitive systemdescribed herein. The cognitive system backend provides a robustcognitive processing and analysis engine that may sit remote from othercomponents, such as those of the airplanes and associated componentsthat provide data to the cognitive system and/or leverage itscapabilities in providing guidance for flight scenarios. An examplecognitive system backend is the IBM Watson line of offerings fromInternational Business Machines Corporation, Armonk, N.Y., U.S.A. Thus,in some examples, this backend is provided as a cloud service hosted ona cloud platform.

Airplane 102 and cognitive system backend 106 are in communication with,and communicate via, one or more intervening networks 108 over wiredand/or wireless communication links 110, such as wired and/or cellular,satellite, Wi-Fi, or other types of wireless connections. Moregenerally, communication links 110 may be any appropriate wirelessand/or wired communication link(s) for communicating data.

FIG. 2 depicts an example conceptual architectural diagram showingcomponents for capture and distribution of experience and knowledgebased on prior flights, in accordance with aspects described herein.Components of FIG. 2 are implemented in some examples as software,hardware, or a combination of the two. Two or more components may beimplemented by a single entity, such as a single computer system ifdesired. In general, the components of FIG. 2 may be distributed as oneor more computers and associated storage components across one or moreairplanes and a cloud facility or other ground facility. Arrows betweencomponents of FIG. 2 represent data communication paths for thecommunication of data therebetween. Example data communication pathsinclude memory, network and/or computer bus connections.

Cognitive system/engine 212 includes various components described below.In some examples, one or more components of cognitive system 212 may beloaded on the airplane, enabling a level of onboard analysis of data andinformation. This may be employed in some cases where the potential fordelays or data corruption resulting from data transmission are aconcern. In other examples, such as those where it is impractical tosupply the needed computational resources onboard the airplane,components of the cognitive system are provided by one or morefacilities remote from the airplane.

External system inputs 214 may be sensors and/or computer systems ofairplanes and the components of cognitive system 212 may be hosted at acloud or other facility on the ground, though in other examples, one ormore components of cognitive system 212 are loaded on the airplanesinstead of being co-located with the other components of the cognitivesystem.

External system inputs 214 refer to inputs that are received by thecognitive system 212 and/or the systems providing such inputs. Theseinclude, for example and not limited to, airplane sensors, indicationsof pilot actions, flight plans, airplane computer systems, inputs fromairplane crews, and data from ground stations. It is noted that theseinputs may include systems that interpret sensor values into morereadable or practical values (e.g. via parsing, organizing, etc.).Though in some examples pure sensor data may be an input, generally puresensor data may not be very manageable. Some systems on airplanes canprocesses the acquired sensor data into useful information that is inputto cognitive system 212.

Fight data, for example in the form of flight parameters, of aircraftflights is obtained from the external system inputs. This is obtained onan ongoing basis as airplane flights are in-process. In-process refersto an ongoing flight, which may encompass the airborne portion of aflight as well activities while on the ground. Thus, flight parametersof current in-process flights are acquired from devices of the airplanesduring in-process flights. Flight parameters include values orproperties of a flight of an actual aircraft during a flight thereof.Examples of flight parameters are values/indications for speed,altitude, and other characteristics of the movement of the aircraft,control values of specific controls of the aircraft, a flight plan,indications of pilot actions, and so forth. These are obtained fromdevices of the aircraft, such as aircraft sensors and/or one or moreaircraft computer systems. Some parameters may be inputs made by a crewmember and indicated to a computer system of the aircraft and thenprovided to the cognitive system.

In some aspects, data from the external system inputs 214 are input tothe data classification module 216. This module can handleclassification of the ‘big data’ from aircrafts, the classificationbeing to facilitate better handling. Classification can include datagrouping, categorizing, tagging or the like for the purpose of easierdata mining, for example. The classification may classify the flightdata according to one or more categories, examples of which includesensor type, airplane type, flight route, weather, and pilot actions, asexamples. Pilot actions may be useful for learning effective andineffective strategies for handling emergency situations, to enableidentification of safe and effective actions to take in given emergencysituations.

Classified data is provided in some aspects to data processing module218, which can process the data by saving relevant information into theknowledge database 222. What is relevant may be dictated by userconfiguration and/or machine learning, as examples. For instance, analtimeter (an example sensor) may provide a data point representingaltitude every 1 second. If altitude remains the same for a relativelylong time, say 10 minutes, data processing module may process thealtitude data for the 10 minutes (600 data points) down to some minimalindication, ‘altitude=X from 14:37.00-14:47.00’. Example data processingincludes sorting and consolidating the classified data.

Data processing module 218 can also save other useful data to an archivedatabase 220. Particular airlines, system administrators, governmentalorganizations, or other stakeholders may desire to save arbitrary dataor other information that is gathered by the cognitive system. The datamay be used by an airline for business purposes, for instance to trackfuel efficiency by pilot in order to award incentives to fuel-efficientpilots. Thus, at least some flight data may be saved/stored/archived toa prespecified database. This may be based on any desired parameters. Asexamples, preconfigured parameters may be specified by an airline orother entity for desired archiving activities. Suggestions for whichflight data to archive may also be presented by the cognitive engine forinstance based on its machine learning, the suggestions being ones thatthe cognitive system ascertains may be helpful or useful aside from itsother objective of identifying preferred flight parameters.

Data is continually collected during flights and therefore includes datacollected before, during, and after actions taken by crew members. Italso reflects flight parameters experienced in both desirable andundesirable situations. The cognitive engine can therefore process theflight data of aircraft flights to build the knowledge database withinformation that is useful, for instance informing of desired orpreferred actions and/or preferred flight parameters in different flightscenarios. If an outcome of a situation was undesirable—a rough landing,abnormal amount of fuel consumption, or poor pilot negotiation ofencountered turbulence, as examples—the processed data can reflect thisto inform of flight parameters and/or pilot actions that should beavoided in the same or similar situations. Conversely, if a particularflight experiences exceptionally low fuel consumption based on anunconventional flight plan, the data will reflect this and the cognitivesystem can identify a correlation between those conditions (flight plan,particular aircraft, weather conditions, etc.) and the resultingefficient fuel consumption.

The knowledge database 222 stores the information informing of preferredactions and flight parameters in different flight scenarios, and can doso with any desired granularity. For example, the information couldcorrespond to a particular airline or any collection of airlines, asingle airplane model or particular airplane models, and so forth. Inthis manner, the data is gathered and analyzed, knowledge is attainedand stored, sorted, or confined to a particular granularity to which itapplies. A best practice for one model airplane may not be applicable toanother model airplane. Knowledge from flights of one airline may beinaccessible to other airlines unless shared, for instance. However, ina particular embodiment, the cognitive system and knowledge database ismade available to any entity that desires it. A third party may maintainthe cognitive system and knowledge thereof may be made available tocustomers (e.g. airlines) that desire the information, for instance.

Accordingly, the cognitive system can maintain preferred flightparameter values indicative of best practices based on sensorinformation and the experience and prior actions of pilots duringprior-experienced flight scenarios. In some aspects, expert orexperienced users may add additional knowledge (e.g. best practices)items to a database. By way of specific example, aircraft manufacturers,users, servicers, or the like may contribute additional best practicesto the system.

The knowledge generated by the system could be used to feed othersystems, such as flight simulator software and/or airline's autopilotfeatures, to convey pilot best practices based on real events.Indications of preferred actions to take and preferred flight parametervalues may be provided to flight simulator software or to controlautopilot features to indicate pilot best practices to take duringsimulated flight scenarios.

Smart engine 224 can obtain flight data from any of a variety ofcomponents, for instance the data processing module 218, classificationmodule 216 or external system inputs themselves. Data that has beenclassified/cleaned may be more immediately useful to the smart engine,though it may be worthwhile for the smart engine to have access to theexternal systems to obtain raw data therefrom if data is missing or isneeded for deeper processing. The knowledge database 222 can alsoprovide useful information used by the smart engine 224 in itsprocessing, as the knowledge database stores the ‘scenario data’ offlights, including one or more in-process flights. In one particularscenario, the smart engine is loaded on a computer system of theaircraft and data from the knowledge database is streamed to it from thecloud.

One role of the smart engine is to check actual data incoming for anin-process flight against the information in the knowledge database tolook for coincidences or similarities between the in-process flight andprior flights for which knowledge has been acquired. This is performedin real-time, i.e. during an in-process flight.

In one aspect, flight parameter values in the knowledge database haveweights assigned to them depending on their relevance. Acquired currentflight parameters (e.g. parameter values) of the in-process flight cambe compared to these weighted flight parameter values in the knowledgedatabase to identify matches. Tolerance levels can be set to dictatewhat constitutes a match between values. For instance, an acquiredflight parameter value of a given parameter (say aircraft speed) may beconsidered a match to a weight flight parameter value in the knowledgedatabase if the two values are within some number of units (15 knots) orvary within some percentage (15%) of each other. There is therefore acorrelation performed of the acquired current flight parameters to theweighted flight parameter values in the knowledge database. Then,weights are established for the acquired current flight parameters basedon the weights of the correlated weighted flight parameter values in theknowledge database. The weights of the current flight parameters arereduced, proportionally in some examples, if the acquired current flightparameter is not the same as the matched flight parameter.

An approach is presented for determining whether current conditionsbeing experienced by an in-process flight correlate to preferred flightactions/parameters that may be suggested. In this approach, the weightsof the current flight parameters are aggregated (e.g. summed) and if thetotal meets or exceeds a threshold, the system will trigger an alert tothe notification module 226. In situations where there are multipleparameters or parameter sets that exceed respective threshold(s), theremay be a relevancy associated with them and their correlated flightactions/parameters, which may be presented sorted by relevance.

Thus, there is a comparison of current flight data/parameters withvalues in the knowledge database to identify matches. The values in theknowledge database are weighted, and those weights are scaled by somefactor when assigning weights to the acquired flight parameter values.The scaling may depend on how close the matching current flight datavalue is to the value in the knowledge database. Then there is adetermination of whether the weights of the weighted acquired currentflight parameters exceed threshold(s) in correlating those acquiredcurrent flight parameters to the flight parameter values in theknowledge database. Exceeding a threshold indicates that the situationbeing currently experienced correlates to one that has been indicated inthe knowledge database. Accordingly, preferred actions and preferredflight parameters may be identified and preferred flight parametervalues provided based on the weighted acquired current flight parametersexceeding such threshold(s).

The thresholds can be set in any desired manner. User/admin input in theform of configuration information of the configuration database 228 isone example. Thresholds can also have different granularities ofapplicability—for example they may be airline-specific or airplane-modelspecific, as examples. They may also be specific to individual pilots orclassification of pilots (beginner, novice, expert). Thresholds may beselected based on type of flight, type of aircraft, a type of route,and/or any other parameter.

Configuration database 228 can also house settings for users like expertor experienced users to adjust the weights of the flight parametervalues in the knowledge database. Other settings, such as maximums,minimums, and baselines settings for values in the knowledge databasecan also be stored in the configuration database.

By way of specific example to illustrate, assume actual data (currentflight parameters of an aircraft during an in-process flight) are thefollowing:

-   -   Wind Speed: 200 km/h;    -   Aircraft weight: 114 Tons;    -   Aircraft type: A320;    -   Route: LA-SJO;

and assume that information in the knowledge database includes thefollowing, with weights indicated in parentheses:

-   -   Wind Speed: 200 km/h (4);    -   Aircraft weight: 110 Tons (8);    -   Aircraft type: : A320 (6);    -   Route: LA -MIA-SJO (8)

Assume the following weights are assigned to the current flightparameters based on the weights of the information in the knowledgedatabase and on a scaling factor for scaling the weights assigned to thecurrent flight parameters:

Current wind speed—weight points: 4 out of 4, because current wind speedis identical to the wind speed in the database;

Aircraft weight—weight points: 6 out of 8 because current aircraftweight is 114 tons, heavier than the 110 tons of the value in thedatabase;

Aircraft type—weight points: 6 out of 6 because current aircraft type isthe same as aircraft type in the database;

Route—Weight points: 7 out of 8 because the current court includes anadditional stop (MIA).

A total coincidence score is the sum of the weights of the currentflight parameters, here 23 points because 4+6+6+7=23.

Assume the established threshold for this correlation is 20 points. Thecoincidence score of 23 meets or exceeds the establish threshold of 20.Therefore, an alert may be triggered with recommendations gleaned fromthe information in the knowledge database, for instance preferredactions/flight parameters associated with the scenario in the knowledgedatabase represented by the flight parameters above.

As noted, recommendations may be to avoid a potentially undesirablesituation or pursue a more desirable situation (e.g. lower fuelconsumption). The recommendations can be ascertained based how/whetherpilot actions and/or flight parameters of those prior flights, asascertained from the gathered data and information stored in theknowledge database, resulted in desirable or undesirable outcomes.Whether particular outcomes, conditions, or parameter values aredesirable or undesirable can be indicated in any of several ways. Theymay be based on administrator input to indicate desirable or undesirablesituations, values, outcomes, etc. They could additionally oralternatively be learned by the cognitive system. For instance, thesystem could learn that particular maneuvers, altitude changes, or anyother information gleaned from flight parameters are not good becausethey result in undesired outcomes. In this regard, there may be are-train process of the system. If a pilot applies a suggestion and theresult is unfavorable, the cognitive system will “learn” from thatexperience and may decrease the points, weight or applicability of therecommendation to that scenario.

Processing can compare acquired current flight parameters to theclassified flight data and identify, in real-time during the in-processflight, and based on threshold(s) in correlations between the acquiredflight parameters and the classified flight data, preferred action(s) totake and preferred flight parameter value(s) for the in-process flightgiven current conditions of the aircraft and surrounding environment asreflected by the acquired current flight parameters. The comparisonnoted constitutes a comparison of the situations (current vs. priorexperienced) based on weight values assigned, to determine whether thesituations are ‘close’, as dictated by the threshold. In someembodiments, the preferred actions to take may be adjustments toparameters (altitude, speed, etc.) of the aircraft and the preferredflight parameter values may be the indications of the where to set thoseparameters. It is understood that many flight parameters may be withinthe control of the pilots or crew, who may manually (or cause a computerto automatically) adjust the parameters. The preferred flight parametervalues can indicate pilot/crew actions in controlling the aircraft.

In this manner, comparing the acquired current flight parameters to theclassified flight data compares the acquired current flight parametersagainst the information in the knowledge database for similarities (e.g.proximity in value) between the current flight parameters and those offlight scenarios reflected in the knowledge database information, toidentify the preferred flight parameter values for the in-processflight.

In another embodiment, a pilot or other user can make ad-hoc requestsfor the cognitive system to look for suggestions based on a currentsituation. As part of the cognitive system's ongoing processing, it maybe continually looking for opportunities to suggest preferred actionsand flight parameters to in-process flights. However, some situationsthat may reflect an obvious emergency to flight personnel may not beautomatically detected as an issue by the cognitive system. Ad-hoc oron-demand requests in such situations can drive the system to considerthe set of conditions in situations that are not necessary observed orrecognized by the system as emergencies. The cognitive system can lookinto the knowledge database for any situations that have similarparameters. In some examples, it may disregard its standard thresholdsand use a different set of thresholds that are configured to allow moreleeway in finding comparable prior situations. If the system identifiessimilar prior situations, it may provide recommendations as describedabove. For instance, it may look for pilot actions in similar situationswith positive or successful results, and present those actions assuggestions to the pilot of the in-process flight. Additionally oralternatively, the ad-hoc requests can serve as a selection by thepilot/crew to record the actions performed by a pilot in the givensituation (such as a pilot-recognized emergency) for furtherreplication. As noted, the cognitive system may not always know that agiven situation was/is an emergency and thus the actions performed bythe pilot may not be recorded as a best practice. Enabling ad-hocrequests as above may dictate to the system that the actions to beperformed in the situation should be observed and potentiallyanalyzed/stored to inform best practices in subsequent flights.

The above ad-hoc approach may be particularly useful in providingguidance or recommendations in emergency situations to less experiencedpilots.

The cognitive system can record pilot actions in the form of recordedflight data for identified emergency situations. At least some of theflight data processed by the system to help build the knowledge databaseincludes these recorded pilot actions and, in some cases, the preferredactions include the recorded pilot actions that were identified asleading to successful or best-case outcomes.

Continuing with the description of FIG. 2, the notification module 226is responsible for displaying/send notifications to pilots/crew members,as triggered by the smart engine 224. Thus, the notification moduleprovides the preferred flight parameter values to the aircraft duringthe in-process flight, and specifically to one or more computer systemsor other devices of the aircraft. In some embodiments, the preferredflight parameter values may be prioritized, sorted by importance, orarranged by applicability to the crew of the aircraft. In this latterregard, suggested parameters or actions may be intended forselection/implementation by different crew members depending on thecontrols or devices under their purview. Additionally or alternatively,in some embodiments the notification module can also provide/displaydata in a remote location, such as a ground facility of the airline(like headquarters) or air control towers, to provide indications toothers outside of the flight personnel who may be interested in knowingthe data provided by the cognitive system to the in-process flight.

The notifications are sent to the aircraft, e.g. a computer systemthereof and preferably in real-time so that corrections or adjustmentscan be made. In some embodiments, some adjustments are madeautomatically, e.g. by a controller or computer system of the aircraft.Ultimately the pilot(s) may have authoritative control over any and alladjustments made based on the preferred or suggested flight parametervalues. In particular embodiments, implementation of adjustment(s) maybe performed based on the presentation of the preferred flight parametervalues to the crew and one or more crew members selecting/confirmingthat the adjustment(s) are to be implemented.

Thus, there may be an implementation of adjustment(s) to one or moredevices of the aircraft to achieve one or more of the received preferredflight parameter values. In other words, there may be some level ofcontrol that the crew exhibits over some flight parameter values of thein-process flight. The control may be realized by making adjustments todevices of the aircraft. By way of example, the angle of attack of thewing flaps may be controlled by the pilot and a received preferredparameter value might indicate a larger angle than a current angle ofattack. An adjustment may be made to the wing flaps by way of anadjustment to the controller thereof In some situations, recommendedactions or parameter values may serve to control aircraft control systemautomatically, i.e. without manual implementation, confirmation, orinvocation by flight personnel.

The feedback module 232 allows administrator/user input to the system inorder to improve (curate) the data and enhance the system and results.Example input includes admin/user specifications or tagging of positiveand negative scenarios and outcomes into the knowledge database. In someaspects, the input may create baselines that the cognitive system usesto learn and then refine over time based on machine learning.

The analytics engine 230 can invoke analytics and generation of reportsas configured by users.

The configuration database 228 can store the relevant configurationsettings of the cognitive system. Weights, thresholds, location (in-airvs. on ground) of software components of the cognitive engine, and anyother settings can be configured in this database. Settings can havediffering granularities in terms of their applicability. For instance asdescribed previously, different configuration settings may be providedfor different airlines or different aircraft, as examples.

FIG. 3 depicts an example conceptual overview of information sharing inthe capture and distribution of experience and knowledge based on priorflights, in accordance with aspects described herein. Many of theaspects depicted in FIG. 3 have been described previously. Knowledge andinformation in the form of data are exchanged between the cognitivesystem 312 and pilots/users 336. For instance, the cognitive systemgathers flight data that includes parameter values and pilot actions forvarious scenarios. The cognitive system leverages the data collection ofonboard sensors and other external system inputs 314 to acquire data.Outputs of the cognitive system flow to ground facilities 334, e.g. airtraffic control stations, cloud computing facilities, data centers orthe like. Meanwhile, information flows to airplanes 302 in the form ofrecommended preferred flight parameter values for possible adjustmentsof in-process flights to achieve desired positive outcomes.

Aspects described herein can improve safety, threat response, andpossibility of positive outcomes in emergency situations by way ofreal-time suggestions, reduce risk based on human errors, enable thesharing of expertise between airline personnel, provide support to thepersonnel crew in case of communication issues with ground stations(e.g. in cases where components of the cognitive system are onboard),leverage real-time information by ground operations to improve theirestimations and plans, and save storage resources by storing relevantinformation, as opposed to the entire collection of gathered raw data,that can be potentially of use in the future.

Aspects can provide real-time recommendations based on currentconditions of an in-process flight and the surrounding environment,create a cognitive knowledge database of best practices based on realsituations faced by pilots, share knowledge and best practices betweenpilots, and record helpful pilot actions in emergency situations, whichactions can be shared and replicated by less experienced users.

Accordingly, data is fed to a cognitive engine to create knowledge thatcan be distributed between key players to help decision-making forin-process flights. Aspects differ from approaches of merely sendinggathered information for offline processing. Sensor and other flightdata is transformed into information and knowledge with the use of acognitive system, then applied to further flight scenarios in real-timefor live guidance.

FIG. 4 depicts an example process for real-time identification andprovision of preferred flight parameters, in accordance with aspectsdescribed herein. In some examples, the process is performed by one ormore computer systems, such as those described herein, which may includeone or more computer systems of an aircraft, one or more cloud servers,and/or one or more other computer systems.

The process begins by obtaining flight data of a plurality of aircraftflights and classifying the flight data according to a plurality ofcategories (402). In some examples, the classifying according to theplurality of categories classifies by one or more of the following:sensor type, aircraft model, flight route, weather, and pilot actions.

The process continues by processing the flight data of the plurality ofaircraft flights by a cognitive engine (404) to build a knowledgedatabase having information informing of a plurality of preferredactions and preferred flight parameters in a plurality of flightscenarios. In some aspects, this processes the classified data todiscard data values of the obtained flight data and obtain the knowledgedatabase information. In some embodiments, at least some flightdata—perhaps some that would otherwise be discarded—is archived to aprespecified database. This archiving feature may be based on aplurality of parameters, for instance preconfigured parameters fordesired archiving activities. Additionally or alternatively, at leastsome of the flight data may be archived based on suggestions presentedby the cognitive engine to an administrator or other user, thesuggestions being ones that the cognitive system ascertains may behelpful or useful aside from the identification of preferred flightparameters.

The process also assigns weights to flight parameter values of theinformation in the knowledge database (406) and acquires current flightparameters from a plurality of devices of an aircraft during anin-process flight (408). The plurality of devices of the aircraftinclude, as examples, sensors and aircraft computer system(s). Theprocess correlates the acquired current flight parameters to theweighted flight parameter values in the knowledge database and alsoestablishes weights for the acquired current flight parameters based onthe weights of the correlated weighted flight parameter values in theknowledge database (410). In some aspects, configuration settings areprovided (for instance by/in the configuration database) for expertusers to adjust the weights and any baseline settings for values in theknowledge database, as desired.

The process compares the acquired current flight parameters to theclassified flight data and identifies, in real-time during thein-process flight, and based on thresholds in correlations between theacquired flight parameters and the classified flight data, one or morepreferred actions to take and preferred flight parameter values for thein-process flight given current conditions of the aircraft andsurrounding environment as reflected by the acquired current flightparameters (412). The preferred flight parameter values may beindicative of best practices based on the experience and prior actionsof pilots, and on sensor information, during prior-experienced flightscenarios. Additionally, the preferred flight parameter values mayindicate pilot actions in controlling the aircraft.

Comparing the acquired current flight parameters to the classifiedflight data can compare the acquired current flight parameters againstthe information in the knowledge database for similarities between thecurrent flight parameters and flight scenarios reflected in theinformation, for example to identify the preferred flight parametervalues for the in-process flight.

In some aspects, a determination is made about whether the weights ofthe weighted acquired current flight parameters exceed one or morethresholds in correlating the acquired current flight parameters to theflight parameter values in the knowledge database. The identification ofthe preferred actions and preferred flight parameters and the providingthe preferred flight parameter values may be performed based on theweighted acquired current flight parameters exceeding the one or morethresholds. Threshold(s) may be selected based on type of flight, typeof aircraft, and/or type of route, as examples.

The method provides the preferred flight parameter values to one or morecomputer systems of the aircraft (414). As an example, the methodpresents the preferred flight parameter values sorted by importance andapplicability to a crew of the aircraft. The process then implements oneor more adjustments to a respective one or more devices of the aircraft,for instance to achieve at least one of the preferred flight parametervalues (416). In some embodiments, the implementation of anyadjustment(s) to device(s) to achieve preferred flight parameters may bemade based on affirmative confirmation from crew members, if desired.For instance, the implementing of the adjustment(s) (416) may beperformed based the presentation (414) of the preferred flightparameters and on receiving a selection from one or more such crewmembers to implement the adjustment(s).

In addition to the above, aspects can record pilot actions in emergencysituations in the form of recorded flight data. Some of the processedflight data can therefore include the recorded pilot actions and atleast some of the preferred actions can include the recorded pilotactions.

As an enhancement, indications of the one or more preferred actions totake and the preferred flight parameter values may be provided to flightsimulator software to indicate pilot best practices to take duringsimulated flight scenarios.

Although various examples are provided, variations are possible withoutdeparting from a spirit of the claimed aspects.

Processes described herein may be performed singly or collectively byone or more computer systems, such as one or more computer systems of acognitive system, one or more computer systems of aircraft, and/or acombination of the foregoing, as examples. FIG. 5 depicts one example ofsuch a computer system and associated devices to incorporate and/or useaspects described herein. A computer system may also be referred toherein as a data processing device/system, computing device/system/node,or simply a computer. The computer system may be based on one or more ofvarious system architectures and/or instruction set architectures, suchas those offered by International Business Machines Corporation (Armonk,N.Y., USA), Intel Corporation (Santa Clara, Calif., USA) or ARM Holdingsplc (Cambridge, England, United Kingdom), as examples.

FIG. 5 shows a computer system 500 in communication with externaldevice(s) 512. Computer system 500 includes one or more processor(s)502, for instance central processing unit(s) (CPUs). A processor caninclude functional components used in the execution of instructions,such as functional components to fetch program instructions fromlocations such as cache or main memory, decode program instructions, andexecute program instructions, access memory for instruction execution,and write results of the executed instructions. A processor 502 can alsoinclude register(s) to be used by one or more of the functionalcomponents. Computer system 500 also includes memory 504, input/output(I/O) devices 508, and I/O interfaces 510, which may be coupled toprocessor(s) 502 and each other via one or more buses and/or otherconnections. Bus connections represent one or more of any of severaltypes of bus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, andnot limitation, such architectures include the Industry StandardArchitecture (ISA), the Micro Channel Architecture (MCA), the EnhancedISA (EISA), the Video Electronics Standards Association (VESA) localbus, and the Peripheral Component Interconnect (PCI).

Memory 504 can be or include main or system memory (e.g. Random AccessMemory) used in the execution of program instructions, storage device(s)such as hard drive(s), flash media, or optical media as examples, and/orcache memory, as examples. Memory 504 can include, for instance, acache, such as a shared cache, which may be coupled to local caches(examples include L1 cache, L2 cache, etc.) of processor(s) 502.Additionally, memory 504 may be or include at least one computer programproduct having a set (e.g., at least one) of program modules,instructions, code or the like that is/are configured to carry outfunctions of embodiments described herein when executed by one or moreprocessors.

Memory 504 can store an operating system 505 and other computer programs506, such as one or more computer programs/applications that execute toperform aspects described herein. Specifically, programs/applicationscan include computer readable program instructions that may beconfigured to carry out functions of embodiments of aspects describedherein.

Examples of I/O devices 508 include but are not limited to microphones,speakers, Global Positioning System (GPS) devices, cameras, lights,accelerometers, gyroscopes, magnetometers, sensor devices configured tosense light, proximity, heart rate, body and/or ambient temperature,blood pressure, and/or skin resistance, and activity monitors. An I/Odevice may be incorporated into the computer system as shown, though insome embodiments an I/O device may be regarded as an external device(512) coupled to the computer system through one or more I/O interfaces510.

Computer system 500 may communicate with one or more external devices512 via one or more I/O interfaces 510. Example external devices includea keyboard, a pointing device, a display, and/or any other devices thatenable a user to interact with computer system 500. Other exampleexternal devices include any device that enables computer system 500 tocommunicate with one or more other computing systems or peripheraldevices such as a printer. A network interface/adapter is an example I/Ointerface that enables computer system 500 to communicate with one ormore networks, such as a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet), providingcommunication with other computing devices or systems, storage devices,or the like. Ethernet-based (such as Wi-Fi) interfaces and Bluetooth®adapters are just examples of the currently available types of networkadapters used in computer systems (BLUETOOTH is a registered trademarkof Bluetooth SIG, Inc., Kirkland, Wash., U.S.A.).

The communication between I/O interfaces 510 and external devices 512can occur across wired and/or wireless communications link(s) 511, suchas Ethernet-based wired or wireless connections. Example wirelessconnections include cellular, Wi-Fi, Bluetooth®, proximity-based,near-field, or other types of wireless connections. More generally,communications link(s) 511 may be any appropriate wireless and/or wiredcommunication link(s) for communicating data.

Particular external device(s) 512 may include one or more data storagedevices, which may store one or more programs, one or more computerreadable program instructions, and/or data, etc. Computer system 500 mayinclude and/or be coupled to and in communication with (e.g. as anexternal device of the computer system) removable/non-removable,volatile/non-volatile computer system storage media. For example, it mayinclude and/or be coupled to a non-removable, non-volatile magneticmedia (typically called a “hard drive”), a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and/or an optical disk drive for reading fromor writing to a removable, non-volatile optical disk, such as a CD-ROM,DVD-ROM or other optical media.

Computer system 500 may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Computer system 500 may take any of various forms,well-known examples of which include, but are not limited to, personalcomputer (PC) system(s), server computer system(s), such as messagingserver(s), thin client(s), thick client(s), workstation(s), laptop(s),handheld device(s), mobile device(s)/computer(s) such as smartphone(s),tablet(s), and wearable device(s), multiprocessor system(s),microprocessor-based system(s), telephony device(s), networkappliance(s) (such as edge appliance(s)), virtualization device(s),storage controller(s), set top box(es), programmable consumerelectronic(s), network PC(s), minicomputer system(s), mainframe computersystem(s), and distributed cloud computing environment(s) that includeany of the above systems or devices, and the like.

Aspects described herein may be incorporated into and/or use a cloudcomputing environment. It is to be understood that although thisdisclosure includes a detailed description on cloud computing,implementation of the teachings recited herein are not limited to acloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes. One such node is node 10 depicted inFIG. 6.

Computing node 10 is only one example of a suitable cloud computing nodeand is not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecomputing nodes 10 with which local computing devices used by cloudconsumers, such as, for example, smartphone or other mobile device 54A,desktop computer 54B, laptop computer 54C, and/or automobile computersystem 54N may communicate. Nodes 10 may communicate with one another.They may be grouped (not shown) physically or virtually, in one or morenetworks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-N shown in FIG. 6 are intended to beillustrative only and that computing nodes 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and cognitive system processing 96.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In addition to the above, one or more aspects may be provided, offered,deployed, managed, serviced, etc. by a service provider who offersmanagement of customer environments. For instance, the service providercan create, maintain, support, etc. computer code and/or a computerinfrastructure that performs one or more aspects for one or morecustomers. In return, the service provider may receive payment from thecustomer under a subscription and/or fee agreement, as examples.Additionally or alternatively, the service provider may receive paymentfrom the sale of advertising content to one or more third parties.

In one aspect, an application may be deployed for performing one or moreembodiments. As one example, the deploying of an application comprisesproviding computer infrastructure operable to perform one or moreembodiments.

As a further aspect, a computing infrastructure may be deployedcomprising integrating computer readable code into a computing system,in which the code in combination with the computing system is capable ofperforming one or more embodiments.

As yet a further aspect, a process for integrating computinginfrastructure comprising integrating computer readable code into acomputer system may be provided. The computer system comprises acomputer readable medium, in which the computer medium comprises one ormore embodiments. The code in combination with the computer system iscapable of performing one or more embodiments.

Although various embodiments are described above, these are onlyexamples. For example, computing environments of other architectures canbe used to incorporate and use one or more embodiments.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising”,when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of one or more embodiments has been presentedfor purposes of illustration and description, but is not intended to beexhaustive or limited to in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain variousaspects and the practical application, and to enable others of ordinaryskill in the art to understand various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A computer-implemented method comprising:obtaining flight data of a plurality of aircraft flights and classifyingthe flight data according to a plurality of categories; acquiringcurrent flight parameters from a plurality of devices of an aircraftduring an in-process flight; comparing the acquired current flightparameters to the classified flight data and identifying one or morepreferred actions to take and preferred flight parameter values for thein-process flight given current conditions of the aircraft andsurrounding environment as reflected by the acquired current flightparameters, wherein the comparing and identifying are performed inreal-time during the in-process flight, and wherein the identifyingcomprises applying pre-set thresholds in identifying whethercorrelations exist between the acquired flight parameters and theclassified flight data, wherein exceeding a pre-set threshold indicatessituational correlation between the in-process flight, as characterizedby the current flight parameters, and a situation indicated by theacquired classified flight data; presenting the preferred flightparameter values sorted by importance and applicability to a crew of theaircraft, and implementing an adjustment to one or more devices of theaircraft to achieve at least one preferred flight parameter value of thepreferred flight parameter values, wherein the implementing is preformedbased on the presenting and on receiving a selection from one or morecrew members to implement the adjustment.
 2. The method of claim 1,wherein the preferred flight parameter values are indicative of bestpractices based on the experience and prior actions of pilots, and onsensor information, during prior-experienced flight scenarios.
 3. Themethod of claim 1, further comprising processing the flight data of theplurality of aircraft flights by a cognitive engine to build a knowledgedatabase having information informing of a plurality of preferredactions and preferred flight parameters in a plurality of flightscenarios.
 4. The method of claim 3, further comprising recording pilotactions in emergency situations in the form of recorded flight data,wherein at least some of the processed flight data comprises therecorded pilot actions and at least some of the preferred actionsinclude the recorded pilot actions.
 5. The method of claim 3, whereinthe comparing the acquired current flight parameters to the classifiedflight data compares the acquired current flight parameters against theinformation in the knowledge database for similarities between thecurrent flight parameters and flight scenarios reflected in theinformation, to identify the preferred flight parameter values for thein-process flight.
 6. The method of claim 5, further comprising:assigning weights to flight parameter values of the information in theknowledge database; correlating the acquired current flight parametersto the weighted flight parameter values in the knowledge database;establishing weights for the acquired current flight parameters based onthe weights of the correlated weighted flight parameter values in theknowledge database; and determining whether the weights of the weightedacquired current flight parameters exceed the pre-set thresholds incorrelating the acquired current flight parameters to the flightparameter values in the knowledge database, wherein the identifying thepreferred actions and preferred flight parameters and the presenting thepreferred flight parameter values is performed based on the weightedacquired current flight parameters exceeding the pre-set thresholds. 7.The method of claim 6, further comprising providing configurationsettings for expert users to adjust the weights and baseline settingsfor values in the knowledge database.
 8. The method of claim 6, whereinthe one or more thresholds are selected based on at least one selectedfrom the group consisting of: a type of flight, type of aircraft, and atype of route.
 9. The method of claim 3, wherein the classifyingaccording to the plurality of categories classifies by at least oneselected from the group consisting of: sensor type, aircraft model,flight route, weather, and pilot actions.
 10. The method of claim 1,wherein the plurality of devices of the aircraft comprise aircraftsensors and one or more aircraft computer systems, wherein the preferredflight parameter values indicate pilot actions in controlling theaircraft, and wherein the method further comprises processing theclassified data to discard data values of the obtained flight data andobtain the knowledge database information.
 11. The method of claim 10,further comprising archiving at least some of the flight data to aprespecified database based on a plurality of parameters.
 12. The methodof claim 11, wherein the at least some of the flight data is archivedbased on suggestions presented by a cognitive engine.
 13. The method ofclaim 1, further comprising providing indications of the one or morepreferred actions to take and the preferred flight parameter values toflight simulator software to indicate pilot best practices to takeduring simulated flight scenarios.
 14. A computer system comprising: amemory; and a processor in communication with the memory, wherein thecomputer system is configured to perform a method comprising: obtainingflight data of a plurality of aircraft flights and classifying theflight data according to a plurality of categories; acquiring currentflight parameters from a plurality of devices of an aircraft during anin-process flight; comparing the acquired current flight parameters tothe classified flight data and identifying one or more preferred actionsto take and preferred flight parameter values for the in-process flightgiven current conditions of the aircraft and surrounding environment asreflected by the acquired current flight parameters, wherein thecomparing and identifying are performed in real-time during thein-process flight, and wherein the identifying comprises applyingpre-set thresholds in identifying whether correlations exist between theacquired flight parameters and the classified flight data, whereinexceeding a pre-set threshold indicates situational correlation betweenthe in-process flight, as characterized by the current flightparameters, and a situation indicated by the acquired classified flightdata; presenting the preferred flight parameter values sorted byimportance and applicability to a crew of the aircraft, and implementingan adjustment to one or more devices of the aircraft to achieve at leastone preferred flight parameter value of the preferred flight parametervalues, wherein the implementing is preformed based on the presentingand on receiving a selection from one or more crew members to implementthe adjustment.
 15. The computer system of claim 14, wherein the methodfurther comprises processing the flight data of the plurality ofaircraft flights by a cognitive engine to build a knowledge databasehaving information informing of a plurality of preferred actions andpreferred flight parameters in a plurality of flight scenarios.
 16. Thecomputer system of claim 15, wherein the method further comprisesrecording pilot actions in emergency situations in the form of recordedflight data, wherein at least some of the processed flight datacomprises the recorded pilot actions and at least some of the preferredactions include the recorded pilot actions.
 17. The computer system ofclaim 15, wherein the comparing the acquired current flight parametersto the classified flight data compares the acquired current flightparameters against the information in the knowledge database forsimilarities between the current flight parameters and flight scenariosreflected in the information, to identify the preferred flight parametervalues for the in-process flight.
 18. The computer system of claim 17,wherein the method further comprises: assigning weights to flightparameter values of the information in the knowledge database;correlating the acquired current flight parameters to the weightedflight parameter values in the knowledge database; establishing weightsfor the acquired current flight parameters based on the weights of thecorrelated weighted flight parameter values in the knowledge database;and determining whether the weights of the weighted acquired currentflight parameters exceed the pre-set thresholds in correlating theacquired current flight parameters to the flight parameter values in theknowledge database, wherein the identifying the preferred actions andpreferred flight parameters and the presenting the preferred flightparameter values is performed based on the weighted acquired currentflight parameters exceeding the pre-set thresholds.
 19. A computerprogram product comprising: a computer readable storage medium readableby at least one processor and storing instructions for execution by theat least one processor for performing a method comprising: obtainingflight data of a plurality of aircraft flights and classifying theflight data according to a plurality of categories; acquiring currentflight parameters from a plurality of devices of an aircraft during anin-process flight; comparing the acquired current flight parameters tothe classified flight data and identifying one or more preferred actionsto take and preferred flight parameter values for the in-process flightgiven current conditions of the aircraft and surrounding environment asreflected by the acquired current flight parameters, wherein thecomparing and identifying are performed in real-time during thein-process flight, and wherein the identifying comprises applying pre-set thresholds in identifying whether correlations exist between theacquired flight parameters and the classified flight data, whereinexceeding a pre-set threshold indicates situational correlation betweenthe in-process flight, as characterized by the current flightparameters, and a situation indicated by the acquired classified flightdata; presenting the preferred flight parameter values sorted byimportance and applicability to a crew of the aircraft, and implementingan adjustment to one or more devices of the aircraft to achieve at leastone preferred flight parameter value of the preferred flight parametervalues, wherein the implementing is preformed based on the presentingand on receiving a selection from one or more crew members to implementthe adjustment.
 20. The computer program product of claim 19, whereinthe method further comprises processing the flight data of the pluralityof aircraft flights by a cognitive engine to build a knowledge databasehaving information informing of a plurality of preferred actions andpreferred flight parameters in a plurality of flight scenarios.
 21. Thecomputer program product of claim 20, wherein the method furthercomprises recording pilot actions in emergency situations in the form ofrecorded flight data, wherein at least some of the processed flight datacomprises the recorded pilot actions and at least some of the preferredactions include the recorded pilot actions.
 22. The computer programproduct of claim 20, wherein the comparing the acquired current flightparameters to the classified flight data compares the acquired currentflight parameters against the information in the knowledge database forsimilarities between the current flight parameters and flight scenariosreflected in the information, to identify the preferred flight parametervalues for the in-process flight.
 23. The computer program product ofclaim 22, wherein the method further comprises: assigning weights toflight parameter values of the information in the knowledge database;correlating the acquired current flight parameters to the weightedflight parameter values in the knowledge database; establishing weightsfor the acquired current flight parameters based on the weights of thecorrelated weighted flight parameter values in the knowledge database;and determining whether the weights of the weighted acquired currentflight parameters exceed the pre-set thresholds in correlating theacquired current flight parameters to the flight parameter values in theknowledge database, wherein the identifying the preferred actions andpreferred flight parameters and the presenting the preferred flightparameter values is performed based on the weighted acquired currentflight parameters exceeding the pre-set thresholds.